In the broader fair machine learning community, several authors advocated for economic concepts [1]}, using inequality indices to quantify and mitigate unfairness [2]}, [3]}, [4]}, [5]}, taking an axiomatic perspective [6]}, [7]}, [8]} or applying welfare economics principles [9]}, [10]}. GGFs, in particular, were recently applied to fair multi-agent reinforcement learning, with multiple reward functions [11]}, [12]}, [13]}. These works consider sequential decision-making problems without ranking, and their GGFs aggregate the objectives of a few agents (typically \(n < 20\) ), while in our ranking problem, there are as many objectives as there are users and items.
